How to Support Developers With AI

Tips for AI-Assisted software development: Treat AI like a pair programmer, not a code vending machine. The most useful mental model for AI-assisted engineering is collaboration. When you see AI as a pair, you stay in control. You guide, review, challenge, and refine. The quality goes up because you’re thinking together, not outsourcing your judgment. It’s the same discipline you’d apply with a human partner. One drives, one navigates. As the navigator, question the code and challenge the assumptions. Do this in practice: - Be the driver. Let AI write code, you focus on architecture, edge cases, and security. - Keep it conversational. Explain your intent, then iterate. Treat prompts as dialogue, not commands. - Ask it to explain its own code. If you can’t follow the explanation, don’t merge the code. - Trust, but verify. Check APIs, versions, and performance assumptions. Run the tests every time. - Use it as a rubber duck. Explaining the problem often reveals the solution. - Challenge suggestions that feel off. Probe edge cases and trade-offs. - Switch who’s driving. Stay engaged so you keep ownership of the code. - Step away when needed. Blind acceptance is a smell, even with AI. Manage the context to stay relevant and focused. - Think of AI as a brilliant, fast and naive developer. Huge range, zero business context, and no common sense about your business. Your job is to pair well.

Most AI coders (Cursor, Claude Code, etc.) still skip the simplest path to reliable software: make the model fail first. Test-driven development turns an LLM into a self-correcting coder. Here’s the cycle I use with Claude (works for Gemini or o3 too): (1) Write failing tests – “generate unit tests for foo.py covering logged-out users; don’t touch implementation.” (2) Confirm the red bar – run the suite, watch it fail, commit the tests. (3) Iterate to green – instruct the coding model to “update foo.py until all tests pass. Tests stay frozen!” The AI agent then writes, runs, tweaks, and repeats. (4) Verify + commit – once the suite is green, push the code and open a PR with context-rich commit messages. Why this works: -> Tests act as a concrete target, slashing hallucinations -> Iterative feedback lets the coding agent self-correct instead of over-fitting a one-shot response -> You finish with executable specs, cleaner diffs, and auditable history I’ve cut debugging time in half since adopting this loop. If you’re agentic-coding without TDD, you’re leaving reliability and velocity on the table. This and a dozen more tips for developers building with AI in my latest AI Tidbits post https://lnkd.in/gTydCV9b

Is AI automating away coding jobs? New research from Anthropic analyzed 500,000 coding conversations with AI and found patterns that every developer should consider: When developers use specialized AI coding tools: - 79% of interactions involve automation rather than augmentation - UI/UX development ranks among the top use cases - Startups adopt AI coding tools at 2.5x the rate of enterprises - Web development languages dominate:          JavaScript/TypeScript: 31%          HTML/CSS: 28% What does this mean for your career? Three strategic pivots to consider: 1. Shift from writing code to "AI orchestration"     If you're spending most of your time on routine front-end tasks, now's the time to develop skills in prompt engineering, code review, and AI-assisted architecture. The developers who thrive will be those who can effectively direct AI tools to implement their vision. 2. Double down on backend complexity     The data shows less AI automation in complex backend systems. Consider specializing in areas that require deeper system knowledge like distributed systems, security, or performance optimization—domains where context and specialized knowledge still give humans the edge. 3. Position yourself at the startup-enterprise bridge     With startups adopting AI coding tools faster than enterprises, there's a growing opportunity for developers who can bring AI-accelerated development practices into traditional companies. Could you be the champion who helps your organization close this gap? How to prepare: - Learn prompt engineering for code generation - Build a personal workflow that combines your expertise with AI assistance - Start tracking which of your tasks AI handles well vs. where you still outperform it - Experiment with specialized AI coding tools now, even if your company hasn't adopted them - Focus your learning on architectural thinking rather than syntax mastery The developer role isn't disappearing—it's evolving. Those who adapt their skillset to complement AI rather than compete with it will find incredible new opportunities. Have you started integrating AI tools into your development workflow? What's working? What still requires the human touch?

AI Tools That Genuinely Boosted My Productivity as a Software Engineer After trying dozens of AI tools over the past few months, I’ve narrowed down the list to a few that truly made a difference in my workflow. These tools have helped me code faster, understand complex systems better, and reduce repetitive tasks. Here are the top ones that stuck with me: 1. GitHub Copilot – For coding assistance  Suggests lines, functions, even entire files.  I use it daily in VS Code to autocomplete logic, generate test cases, and eliminate boilerplate code. 2. CodeWhisperer by AWS – Secure code generation  An AWS-native alternative to Copilot, focused on security and privacy.  It’s extremely helpful when integrating AWS SDKs and working on backend services. 3. Phind – Dev-specific AI search  This replaced Google for me when it comes to technical questions.  Phind gives concise, accurate answers for framework issues, error debugging, and best practices. 4. Tabnine – Secure and private code completion  Great when you’re working with sensitive or proprietary code.  Runs on-prem and supports a wide range of languages and IDEs. 5. Codeium – Lightweight code autocomplete  A fast and free alternative to Copilot.  I use it for side projects, and it performs well with multiple languages and frameworks. 6. Cody by Sourcegraph – Chat with your codebase  Lets me ask questions like “What does this function do?” or “Where is this used?”  It’s a major help when exploring large or legacy codebases. These tools helped me: Debug faster Refactor smarter Document better Ship cleaner code If you're a developer and haven’t explored these yet, start with GitHub Copilot or Phind. They’re game changers. What AI tools are you currently using in your dev stack? Always open to trying more. Follow Abhay Singh for more such reads.

I shipped 100,000 lines of high-quality code in 2 weeks using AI coding agents. But here's what nobody talks about: we're deploying AI coding tools without the infrastructure they need to actually work. When we onboard a developer, we give them documentation, coding standards, proven workflows, and collaboration tools. When we "deploy" a coding agent, we give them nothing and ask them to spend time changing their behavior and workflows on top of actively shipping code. So I compiled what I'm calling AI Coding Agent Infrastructure or the missing support layer: • Skills with mandatory skill checking that makes it structurally impossible for agents to rationalize away test-driven development (TDD) or skip proven workflows (Credits: Superpowers Framework by Jesse Vincent, Anthropic Skills, custom prompt-engineer skill based on Anthropic’s prompt engineering overview). • 114+ specialized sub-agents that work in parallel (up to 50 at once) like Backend Developer + WebSocket Engineer + Database Optimizer running simultaneously, not one generalist bottleneck (Credits: https://lnkd.in/dgfrstVq) • Ralph method for overnight autonomous development (Credits: Geoffrey Huntley, repomirror project https://lnkd.in/dXzAqDGc) This helped drive my coding agent output from inconsistent to 80% of the way there, enabling me to build at a scale like never before. Setup for this workflow takes you 5 minutes. A single prompt installs everything across any AI coding tool (Cursor, Windsurf, GitHub Copilot, Claude Code). I'm open sourcing the complete infrastructure and my workflow instructions today. We need better developer experiences than being told to "use AI tools" or manually put all of these pieces together without the support layer to make them actually work. PRs are welcome, whether you're building custom skills, creating domain-specific sub-agents, or finding better patterns. Link to repo: https://lnkd.in/dfm4NAmh Full breakdown of workflow here: https://lnkd.in/dr9c-UX3 What patterns have you found make the biggest difference in your coding agent productivity?

Last week, a junior dev messaged me: “Everyone talks about Cursor and Copilot... But are there any less hyped AI tools that are actually useful?” Great question. Because the AI space is bigger than just the usual names. 👇 So I shared this curated list of 10 underrated AI tools for developers in 2025 — Not just trendy, but genuinely useful across coding, design, automation, and workflows. Here it is 🧵 1️⃣ Relume AI: (https://relume.io) -> Instantly build sitemaps and wireframes. Export to Figma, React, or HTML. 2️⃣ Amazon CodeWhisperer: (https://lnkd.in/dPpRFDvG) -> Context-aware code suggestions with a security-first mindset. 3️⃣ DeepCode: (https://www.deepcode.ai) -> AI code reviews that catch bugs and improve code quality. 4️⃣ Codeium: (https://codeium.com) -> Lightweight AI assistant with fast, real-time coding suggestions. 5️⃣ Visual Studio IntelliCode: (https://lnkd.in/dNd8EdHK) -> Smarter auto-complete and bug spotting inside VS. 6️⃣ GitHub AI Coding Agent: (https://lnkd.in/dQ5XYTY8) -> Agentic coding assistant beyond Copilot’s autocomplete. 7️⃣ Dataiku AI Studio: (https://www.dataiku.com) -> Build ML workflows and automate with agentic AI. 8️⃣ Google Conversational Agents Console: (https://lnkd.in/dAeZbB_j) -> Create AI-powered chat agents with Google Cloud. 9️⃣ ServiceNow AI Agent: (https://lnkd.in/dEhKeWg9) -> Automate workflows using natural language understanding. 🔟 Snowflake Snowpark AI: (https://lnkd.in/dd-ANy6b) -> AI + ML for your data engineering inside Snowflake. These aren’t the loudest tools on the internet— But they are helping devs build faster, cleaner, and smarter. 💬 Have you used any of these? Or do you have an underrated AI tool in your stack? Drop it in the comments — I’m always adding to my toolkit! P.S. Tag a developer who’s curious about AI but tired of hearing the same 3 tools everywhere. - Ghazi Khan | Follow for more #AItools #Developers #Productivity #CodeSmarter #BuildWithAI #UnderratedTools #DevLife2025

🧠 I still remember the first time I sent my code for review. At the time, I was part of a test automation team. There were no tools, no AI, just good old-fashioned programming methods. I had written 120 lines of code to update a configuration file. I felt proud that it worked, it passed the code review checklist, and I had built it from scratch. I sent it to one of our senior architects. He said: “This is well written. But… you can do this in just four lines using Win32 APIs.” 😅 That moment stuck with me not because I was wrong, but because of how kindly he taught me something better. That’s the power of a good code review. Years later, I started working with fast-moving startups. And something changed. Reviews were missing. The code got pushed straight to production. Late-night fixes became the norm. Some retroactive reviews were done, but it was too late by then. Not because people didn’t care, but because they were out of time. a. Developers were buried under features. b. Tech leads had no bandwidth. c. The CTOs often bypassed the process when pushing code. So, reviews were skipped to “move fast.” And that’s how bugs sneak in. Small today. Big tomorrow. But here’s the good news: Code reviews don’t have to slow you down anymore. Tools like CodeRabbit can now be used as your AI reviewer. They review pull requests instantly, provide helpful comments, and even explain changes in simple language. They don’t just check for errors; they learn from your code, flag risky patterns, and help teams stay on track. 💡 The way we review code has changed. And that’s a good thing. AI isn’t here to replace developers. It’s here to support them, especially when teams are small and deadlines are tight. Don't skip code reviews if you're working in a startup or lean team. Let AI help. Let your developers breathe. Because the best bugs are the ones you never ship. 👇 Have you used AI for code reviews yet? I would love to hear your thoughts. #SoftwareTesting #QualityAssurance #CodeReviews #TestMetry

🚨 𝐂𝐨𝐝𝐞 𝐟𝐚𝐬𝐭𝐞𝐫. 𝐒𝐡𝐢𝐩 𝐦𝐨𝐫𝐞. 𝐁𝐮𝐫𝐧 𝐨𝐮𝐭 𝐲𝐨𝐮𝐫 𝐭𝐞𝐚𝐦. 𝐒𝐨𝐮𝐧𝐝 𝐟𝐚𝐦𝐢𝐥𝐢𝐚𝐫? 🚨 Everyone's chasing AI-driven productivity. Tools like GitHub Copilot promise faster code, fewer bottlenecks, and leaner workflows. But in her brilliant piece for SiliconANGLE, Rachel Laycock issues a powerful reminder: 💡 Software engineering isn't about how fast we write code. It's about how well we solve problems. Here’s what stood out: 🔍 𝐓𝐡𝐞 𝐌𝐲𝐭𝐡 𝐨𝐟 𝐀𝐈-𝐃𝐫𝐢𝐯𝐞𝐧 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲 AI can’t shortcut the creative, collaborative process that makes engineering impactful. More code ≠ more value. 🏭 𝐅𝐥𝐚𝐰𝐞𝐝 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐚𝐥-𝐄𝐫𝐚 𝐌𝐢𝐧𝐝𝐬𝐞𝐭 Treating code like widgets on an assembly line leads to the wrong incentives and piles of technical debt. 🤖 𝐓𝐡𝐞 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 𝐨𝐟 𝐀𝐈 𝐢𝐧 𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐲 AI shines at automating the boring stuff, test generation, boilerplate. But it’s no replacement for human insight or design thinking. 🔁 𝐀 𝐒𝐡𝐢𝐟𝐭 𝐢𝐧 𝐏𝐞𝐫𝐬𝐩𝐞𝐜𝐭𝐢𝐯𝐞 Just like the move from assembly to high-level languages, AI will change how we work. Not eliminate developers altogether. 💡 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐓𝐢𝐩𝐬 𝐭𝐨 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞 𝐀𝐈 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐁𝐫𝐞𝐚𝐤𝐢𝐧𝐠 𝐂𝐮𝐥𝐭𝐮𝐫𝐞: 1. Automate the Mundane. Let AI handle the repetitive; humans focus on creativity. 2. Prioritize Quality Over Speed: Shift metrics from volume to value. 3. Treat AI as an Assistant, Not an Authority: Use it to spark ideas, not dictate solutions. 4. Foster Critical Thinking: Build teams that learn, question, and mentor. ⚠️ Let’s stop chasing productivity myths and start reinforcing engineering cultures grounded in curiosity, craftsmanship, and human intelligence. 🔗Link to the article can be found in the comments. #AI #SoftwareEngineering #Culture #Productivity #DevLeadership